おそらく学部４年時代に3年生向けにでっちあげたRの資料。

1. R graphics
∼programingⅣ∼
2012/12/12
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2. Demo
demo()を使ってみよう！
対話環境で
> demo(graphics)
graphics 伝統的なトレリスグラフィックス
image 画像に関するデモ
plotmath 数式描写のデモ
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7. Graphics Device
OSや画像によっていろいろ
ここに描写
4

8. 作図関数
高水準作図関数
低水準作図関数
hist() : ヒストグラム
plot() : 散布図
barplot() : 棒グラフ
text() : 座標にラベルを描写
title() : タイトルを描写
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9. 作図の流れ
高水準作図関数で図形を描写
低水準作図関数で文字などを追加
*大抵オプションでできる...........
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10. ヒストグラム hist()
> hist(iris$Sepal.Length)
irisデータのSepal.Lengthを使って
ヒストグラムを作成
> str(iris)
irisのデータ構造を表示させる
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11. 少し頑張ったヒストグラム
> hist(iris[iris$Species=="setosa",]$Sepal.Length,
# SpeciesがsetosaのSepal.Lengthを使う
col="blue", # 色は青
main="setosa", # タイトルはsetosa
xlim=range(iris$Sepal.Length),
# x軸はSepal.Lengthの範囲
xlab="Sepal.Length") # x軸のラベル
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12. 散布図 plot()
> plot(iris[,1],iris[,3])
#plot(x軸のデータ、y軸のデータ)
第１変数Sepal.Lengthと
第３変数Petal.Lengthの散布図
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13. plot()の形式
1 > plot(iris[,1], type=”s”)
plot(数値ベクトル,オプション)
2 > plot(iris[,1], iris[,2], type=”n”)
plot(数値ベクトル1,数値ベクトル2,オプション)
3 > plot(matrix(1:12, nrow=6, ncol=2),type="l")
plot(行列, オプション)
4 > plot(iris, main="iris Data")
plot(データフレーム, オプション)
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14. 低水準作図関数をつかってみる
> plot(iris[,1],iris[,3],type="n")
# type=”n” 軸以外なにも描かない
> text(iris[,1],iris[,3])
# サンプル番号の散布図を作成
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15. 描画オプション
type プロットの形式 “p”(点) “l”(直線)......
col 色の指定 “red”,”blue”.........
main メインタイトルの指定 “Apple”
sub サブタイトルの指定 “2012/12/5”
x(y)lab x(y)軸のタイトルの指定 “time”
他にもいろいろ
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16. 図の保存
> pdf(file="test.pdf", width=6, height=4)
# Graphic Deviceをオープン
> plot(iris[,2], type="l", main="TestGraphics")
# 図を描写
> dev.off()
# Graphic Deviceをクローズ
ワーキングディレクトリに保存される
必ず！
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17. 出力方法
pdf() pdf形式
pictex() texで使える形式
jpeg() jpeg形式
png() png形式
bitmap() ビットマップ形式
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18. k-menasの結果表示
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> data(iris)
> mi <- iris[1:4]
> ans <- iris[5]
> km <- kmeans(mi,3,20)
> plot(iris[,1],iris[,2], col=km$cluster)
データ番号に色を自動で割り振ってくれる

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4.5 5.0 5.5 6.0 6.5 7.0 7.5 8.0
2.02.53.03.54.0
iris[, 1]
iris[,2]

20. 主成分分析
主成分分析 prcomp
> data(iris)
> data <- iris[1:4]
> prcomp.obj <- prcomp(data,scale=TRUE)
> pc1 <- prcomp.obj$x[,1]
> pc2 <- prcomp.obj$x[,2]
主成分分析 : 次元が多くてグラフの描けないようなデータを
2次元など低次元に要約
#irisの1-4列のデータを使用
# 第一主成分得点
# 第二主成分得点
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21. 分析結果の表示
plotを使って作図
> label <- iris[,5]
> plot(pc1, pc2, col = label, main = "result")
# 3種類のラベルに色を割り振って作図
★おまけ
biplot(prcomp.obj)でも作図できます
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−3 −2 −1 0 1 2 3
−2−1012
result
pc1
pc2
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23. 20
coution
dev.off()したら図が出なくなった
色
• Graphic Deviceを開いてからplot → x11()
•Rを起動しなおす
• クラスタ数が多すぎると色が足りないかも

24. 参考資料
R-Tips
http://cse.naro.affrc.go.jp/takezawa/r-tips/r.html
プログラミングR 「基礎からグラフィックスまで」
統計科学研究所HP
http://www.statistics.co.jp/
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